Table of Contents
- 1 Data Description
- 2 Business Problem
- 3 Introduction to Boosting
- 4 Colab
- 5 Imports
- 6 Useful Scripts
- 7 Load the data
- 8 Train test split with stratify
- 9 Train Validation with stratify
- 10 Modelling catboost
- 11 Catboost with validation set
- 12 Feature Statistics
- 13 Feature Importance
- 14 catboost using Pool
- 15 Cross Validation
- 16 HPO (Hyper Parameter Optimization)
- 17 Best Model
- 18 Time Taken
Kernel Author:
Bhishan Poudel, Data Scientist, Ph.D Astrophysics .
Bhishan Poudel, Data Scientist, Ph.D Astrophysics .
Data Description¶
The datasets contains transactions made by credit cards in September 2013 by european cardholders.
This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions.
The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation.
Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data.
Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'.
Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning.
Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.m
Business Problem¶
Business Problem:
Task : Detect the fraudulent activities.
Metric : Recall
Sampling: No sampling, use all the data.
Tools: Use python module Pycaret for classification.
Question: How many frauds are correctly classified?
Task : Detect the fraudulent activities.
Metric : Recall
Sampling: No sampling, use all the data.
Tools: Use python module Pycaret for classification.
Question: How many frauds are correctly classified?
Introduction to Boosting¶
The term Boosting refers to a family of algorithms which converts weak learner to strong learners.
There are many boosting algorithms:
sklearn.ensemble.GradientBoostingRegressor
xgboost.XGBRegressor # fast and best
lightgbm.LGBMRegressor # extreme fast, little acc than xgb
catboost.CatBoostRegressor # good for categorical featsColab¶
In [49]:
%%capture
import sys
ENV_COLAB = 'google.colab' in sys.modules
if ENV_COLAB:
#!pip install hpsklearn
!pip install shap eli5 lime scikit-plot watermark
!pip install optuna hyperopt
!pip install catboost
!pip install ipywidgets
!pip install -U scikit-learn
!jupyter nbextension enable --py widgetsnbextension
# create project like folders
!mkdir -p ../outputs ../images ../reports ../html ../models
print('Environment: Google Colab')
Imports¶
In [14]:
import time
notebook_start_time = time.time()
In [15]:
import numpy as np
import pandas as pd
SEED = 100
# visualizatioin
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = 8,8
plt.rcParams.update({'font.size': 16})
plt.style.use('ggplot')
%matplotlib inline
import seaborn as sns
sns.set(color_codes=True)
# six and pickle
import six
import pickle
import joblib
# mixed
import copy
import pprint
pp = pprint.PrettyPrinter(indent=4)
# sklearn
import sklearn
# classifiers
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
# scale and split
from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
# sklearn scalar metrics
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score
# roc auc and curves
from sklearn.metrics import auc
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import precision_recall_curve
# confusion matrix and classification report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
# boosting
import xgboost, lightgbm, catboost
import xgboost as xgb
import lightgbm as lgb
from sklearn.ensemble import GradientBoostingRegressor
from xgboost import XGBClassifier, DMatrix
from lightgbm import LGBMClassifier, Dataset
from catboost import CatBoostClassifier, Pool, CatBoost
# parameters tuning
from hyperopt import hp, tpe, fmin, Trials, STATUS_OK, STATUS_FAIL
from hyperopt.pyll import scope
from hyperopt.pyll.stochastic import sample
# model intepretation modules
import eli5
import shap
import yellowbrick
import lime
import scikitplot
# version
%load_ext watermark
%watermark -a "Bhishan Poudel" -d -v -m
print()
%watermark -iv
Useful Scripts¶
In [16]:
def show_method_attributes(obj, ncols=7,start=None, inside=None):
""" Show all the attributes of a given method.
Example:
========
show_method_attributes(list)
"""
lst = [elem for elem in dir(obj) if elem[0]!='_' ]
lst = [elem for elem in lst
if elem not in 'os np pd sys time psycopg2'.split() ]
if isinstance(start,str):
lst = [elem for elem in lst if elem.startswith(start)]
if isinstance(start,tuple) or isinstance(start,list):
lst = [elem for elem in lst for start_elem in start
if elem.startswith(start_elem)]
if isinstance(inside,str):
lst = [elem for elem in lst if inside in elem]
if isinstance(inside,tuple) or isinstance(inside,list):
lst = [elem for elem in lst for inside_elem in inside
if inside_elem in elem]
return pd.DataFrame(np.array_split(lst,ncols)).T.fillna('')
In [112]:
def model_evaluation(model_name, desc, ytest, ypreds,df_eval=None,
show=True,sort_col='Recall'):
if df_eval is None:
df_eval = pd.DataFrame({'Model': [],
'Description':[],
'Accuracy':[],
'Precision':[],
'Recall':[],
'F1':[],
'AUC':[],
})
# model evaluation
average = 'binary'
row_eval = [model_name,desc,
sklearn.metrics.accuracy_score(ytest, ypreds),
sklearn.metrics.precision_score(ytest, ypreds, average=average),
sklearn.metrics.recall_score(ytest, ypreds, average=average),
sklearn.metrics.f1_score(ytest, ypreds, average=average),
sklearn.metrics.roc_auc_score(ytest, ypreds),
]
df_eval.loc[len(df_eval)] = row_eval
df_eval = df_eval.drop_duplicates()
df_eval = df_eval.sort_values(sort_col,ascending=False)
if show:
display(df_eval.style.background_gradient(subset=[sort_col]))
return df_eval
df_eval = None
Load the data¶
In [19]:
ifile = 'https://github.com/bhishanpdl/Datasets/blob/master/Projects/Fraud_detection/raw/creditcard.csv.zip?raw=true'
df = pd.read_csv(ifile,compression='zip')
print(df.shape)
df.head()
Out[19]:
In [20]:
target = 'Class'
features = df.columns.drop(target)
df[target].value_counts(normalize=True)*100
Out[20]:
In [82]:
sns.countplot(x=df[target])
Out[82]:
Train test split with stratify¶
In [21]:
from sklearn.model_selection import train_test_split
df_Xtrain_orig, df_Xtest, ser_ytrain_orig, ser_ytest = train_test_split(
df.drop(target,axis=1),
df[target],
test_size=0.2,
random_state=SEED,
stratify=df[target])
ytrain_orig = ser_ytrain_orig.to_numpy().ravel()
ytest = ser_ytest.to_numpy().ravel()
print(df_Xtrain_orig.shape)
df_Xtrain_orig.head()
Out[21]:
Train Validation with stratify¶
In [22]:
df_Xtrain, df_Xvalid, ser_ytrain, ser_yvalid = train_test_split(
df_Xtrain_orig,
ser_ytrain_orig,
test_size=0.2,
random_state=SEED,
stratify=ser_ytrain_orig)
ytrain = ser_ytrain.to_numpy().ravel()
yvalid = ser_yvalid.to_numpy().ravel()
print(df_Xtrain.shape)
Modelling catboost¶
https://catboost.ai/docs/concepts/python-reference_catboostregressor.html
class CatBoostRegressor(
iterations=None, learning_rate=None,
depth=None, l2_leaf_reg=None,
model_size_reg=None, rsm=None,
loss_function='RMSE', border_count=None,
feature_border_type=None, per_float_feature_quantization=None,
input_borders=None, output_borders=None,
fold_permutation_block=None, od_pval=None,
od_wait=None, od_type=None,
nan_mode=None, counter_calc_method=None,
leaf_estimation_iterations=None, leaf_estimation_method=None,
thread_count=None, random_seed=None,
use_best_model=None, best_model_min_trees=None,
verbose=None, silent=None,
logging_level=None, metric_period=None,
ctr_leaf_count_limit=None, store_all_simple_ctr=None,
max_ctr_complexity=None, has_time=None,
allow_const_label=None, one_hot_max_size=None,
random_strength=None,name=None, ignored_features=None,
train_dir=None, custom_metric=None,
eval_metric=None, bagging_temperature=None,
save_snapshot=None, snapshot_file=None,
snapshot_interval=None, fold_len_multiplier=None,
used_ram_limit=None, gpu_ram_part=None,
pinned_memory_size=None, allow_writing_files=None,
final_ctr_computation_mode=None, approx_on_full_history=None,
boosting_type=None, simple_ctr=None,
combinations_ctr=None, per_feature_ctr=None,
ctr_target_border_count=None, task_type=None,
device_config=None, devices=None,
bootstrap_type=None, subsample=None,
sampling_unit=None, dev_score_calc_obj_block_size=None,
max_depth=None, n_estimators=None,
num_boost_round=None, num_trees=None,
colsample_bylevel=None, random_state=None,
reg_lambda=None, objective=None,
eta=None, max_bin=None,
gpu_cat_features_storage=None, data_partition=None,
metadata=None, early_stopping_rounds=None,
cat_features=None, grow_policy=None,
min_data_in_leaf=None, min_child_samples=None,
max_leaves=None, num_leaves=None,
score_function=None, leaf_estimation_backtracking=None,
ctr_history_unit=None, monotone_constraints=None
)
In [24]:
import catboost
show_method_attributes(catboost,2)
Out[24]:
In [25]:
from catboost import CatBoostClassifier, Pool
show_method_attributes(CatBoostClassifier,2)
Out[25]:
In [87]:
from catboost import CatBoostClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.metrics import accuracy_score, precision_score, recall_score,f1_score
from sklearn.metrics import confusion_matrix
# time
time_start = time.time()
# current parameters
desc = 'default,random_state=100, cross_validation_ypreds'
Xtr = df_Xtrain.to_numpy()
ytr = ser_ytrain.to_numpy().ravel()
Xtx = df_Xtest.to_numpy()
ytx = ser_ytest.to_numpy().ravel()
# fit the model
model = CatBoostClassifier(verbose=100,random_state=SEED)
model.fit(Xtr, ytr)
# save the model
# joblib.dump(model_cat, 'model_cat.pkl')
# model_cat = joblib.load('model_cat.pkl')
# predictions
skf = StratifiedKFold(n_splits=2,shuffle=True,random_state=SEED)
ypreds_cv = cross_val_predict(model, Xtx, ytx, cv=skf)
ypreds = ypreds_cv
# model evaluation
df_eval = model_evaluation('catboost', desc, ytx,ypreds,df_eval=df_eval)
time_taken = time.time() - time_start
print('Time taken: {:.0f} min {:.0f} secs'.format(*divmod(time_taken,60)))
display(df_eval)
In [109]:
%%time
model = CatBoostClassifier(verbose=100,random_state=SEED)
model.fit(Xtr, ytr)
joblib.dump(model, '../models/model_cat_default_seed100.joblib')
ypreds = model.predict(Xtx)
cm = sklearn.metrics.confusion_matrix(ytx,ypres)
print('confusion matrix\n',cm)
desc = 'default, seed=100'
df_eval = model_evaluation('catboost', desc, ytx,ypreds,df_eval=df_eval)
In [89]:
yprobs = model.predict_proba(Xtx)
print(yprobs[:5])
In [93]:
from scikitplot import metrics as skpmetrics
skpmetrics.plot_confusion_matrix(ytx, ypreds)
Out[93]:
In [95]:
fig, ax = plt.subplots(figsize=(12,8))
skpmetrics.plot_roc(ytx,yprobs,ax=ax)
Out[95]:
In [28]:
import eli5
# eli5.explain_weights_catboost(model) # same thing
eli5.show_weights(model)
Out[28]:
Catboost with validation set¶
In [29]:
df_Xtrain.head(2)
Out[29]:
In [107]:
# # time
# time_start = time.time()
# # current parameters
# Xtr = df_Xtrain
# ytr = ser_ytrain.to_numpy().ravel()
# Xtx = df_Xtest
# ytx = ser_ytest.to_numpy().ravel()
# Xvd = df_Xvalid
# yvd = ser_yvalid.to_numpy().ravel()
# # fit the model
# model = CatBoostClassifier(random_state=0,verbose=100)
# model.fit(Xtr, ytr,
# eval_set=(Xvd, yvd))
# # ypreds
# ypreds = model.predict(Xtx)
# # r-squared values
# auc = roc_auc_score(ytx, ypreds)
# # time
# time_taken = time.time() - time_start
# print('Time taken: {:.0f} min {:.0f} secs'.format(*divmod(time_taken,60)))
# print('ROC AUC Score ', auc)
Feature Statistics¶
catboost tutorials model analysis feature statistics tutorial
In [31]:
# float feature
feature_name = 'Amount'
dict_stats = model.calc_feature_statistics(df_Xtrain, ser_ytrain, feature_name, plot=True)
Feature Importance¶
In [32]:
# feature importance
df_imp = pd.DataFrame({'Feature': features,
'Importance': model.feature_importances_
})
df_imp.sort_values('Importance',ascending=False).style.background_gradient()
Out[32]:
In [33]:
def plot_feature_imp_catboost(model_catboost,features):
"""Plot the feature importance horizontal bar plot.
"""
df_imp = pd.DataFrame({'Feature': model.feature_names_,
'Importance': model.feature_importances_
})
df_imp = df_imp.sort_values('Importance').set_index('Feature')
ax = df_imp.plot.barh(figsize=(12,8))
plt.grid(True)
plt.title('Feature Importance',fontsize=14)
ax.get_legend().remove()
for p in ax.patches:
x = p.get_width()
y = p.get_y()
text = '{:.2f}'.format(p.get_width())
ax.text(x, y,text,fontsize=15,color='indigo')
plt.show()
plot_feature_imp_catboost(model, features)
In [34]:
df_fimp = model.get_feature_importance(prettified=True)
df_fimp.head()
Out[34]:
In [35]:
plt.figure(figsize=(12,8))
ax = sns.barplot(x=df_fimp.columns[1], y=df_fimp.columns[0], data=df_fimp);
for p in ax.patches:
x = p.get_width()
y = p.get_y()
text = '{:.2f}'.format(p.get_width())
ax.text(x, y,text,fontsize=15,color='indigo',va='top',ha='left')
catboost using Pool¶
In [36]:
from catboost import CatBoost, Pool
In [37]:
# help(CatBoost)
In [38]:
cat_features = [] # take it empty for the moment
dtrain = Pool(df_Xtrain, ser_ytrain, cat_features=cat_features)
dvalid = Pool(df_Xvalid, ser_yvalid, cat_features=cat_features)
dtest = Pool(df_Xtest, ser_ytest, cat_features=cat_features)
In [39]:
params_cat = {'iterations': 100, 'verbose': False,
'random_seed': 0,
'eval_metric':'AUC',
'loss_function':'Logloss',
'cat_features': [],
'ignored_features': [],
'early_stopping_rounds': 200,
'verbose': 200,
}
bst_cat = CatBoost(params=params_cat)
bst_cat.fit(dtrain,
eval_set=(df_Xvalid, ser_yvalid),
use_best_model=True,
plot=True);
print(bst_cat.eval_metrics(dtest, ['AUC'])['AUC'][-1])
Cross Validation¶
cv(pool=None, params=None, dtrain=None, iterations=None,
num_boost_round=None, fold_count=None, nfold=None, inverted=False,
partition_random_seed=0, seed=None, shuffle=True, logging_level=None,
stratified=None, as_pandas=True, metric_period=None, verbose=None,
verbose_eval=None, plot=False, early_stopping_rounds=None,
save_snapshot=None, snapshot_file=None,
snapshot_interval=None, folds=None, type='Classical')
In [40]:
params = {'iterations': 100, 'verbose': False,
'random_seed': 0,
'loss_function':'Logloss',
'eval_metric':'AUC',
}
df_scores = catboost.cv(dtrain,
params,
fold_count=2,
verbose=100,
shuffle=True,
stratified=True,
plot="True") # plot does not work in google colab
In [41]:
print(df_scores.columns)
df_scores.head()
Out[41]:
In [42]:
sns.lineplot(x='iterations',y='train-Logloss-mean',data=df_scores,ax=ax,color='r')
sns.lineplot(x='iterations',y='test-Logloss-mean',data=df_scores,ax=ax,
color='b',alpha=0.2,linewidth=5,linestyle='--')
plt.show()
HPO (Hyper Parameter Optimization)¶
We generally should optimize model complexity and then tune the convergence.
model complexity: max_depth etc convergence: learning rate
Parameters:
- learning_rate: step size shrinkage used to prevent overfitting. Range is [0,1]
- depth: determines how deeply each tree is allowed to grow during any boosting round.
- subsample: percentage of samples used per tree. Low value can lead to underfitting.
- colsample_bytree: percentage of features used per tree. High value can lead to overfitting.
Baseline model¶
In [110]:
model = joblib.load('../models/model_cat_default_seed100.joblib')
ypreds = model.predict(df_Xtest)
cm = confusion_matrix(ytest, ypreds)
print(cm)
Using Early Stopping from Validation Set¶
In [103]:
%%time
params = dict(verbose=500,
random_state=0,
iterations=3_000,
eval_metric='AUC',
cat_features = [],
early_stopping_rounds=200,
)
model = catboost.CatBoostClassifier(**params)
model.fit(df_Xtrain, ytrain,
eval_set=(df_Xvalid, yvalid),
use_best_model=True,
plot=False
);
# now use the best iteration
best_iter = model.get_best_iteration()
model = CatBoostClassifier(verbose=False,random_state=0,iterations=best_iter)
model.fit(df_Xtrain, ser_ytrain)
joblib.dump(model, '../models/model_cat_earlystopping.joblib')
ypreds = model.predict(df_Xtest)
cm = confusion_matrix(ytest, ypreds)
print(cm)
desc = f'early stopping, iterations={best_iter}'
df_eval = model_evaluation('catboost', desc, ytx,ypreds,df_eval=df_eval)
# using best iterations is worse, use previous 1000.
In [104]:
# for n in [6]: # default detpth = 6
# model = CatBoostClassifier(verbose=False,random_state=0,
# iterations=1_000,
# depth=n,
# )
# model.fit(Xtr, ytr)
# ypreds = model.predict(Xtx)
# cm = confusion_matrix(ytest, ypreds)
# error = cm[0,1] + cm[1,0]
# print(f'Confusion matrix error count = {error} for n = {n}')
Try Your luck with different random states¶
In [105]:
# for n in [0]:
# model = CatBoostClassifier(verbose=False,random_state=n,
# depth=6,
# iterations=1_000,
# )
# model.fit(Xtr, ytr)
# ypreds = model.predict(Xtx)
# cm = confusion_matrix(ytest, ypreds)
# error = cm[0,1] + cm[1,0]
# print(f'Confusion matrix error count = {error} for n = {n}')
HPO Hyper Parameter Optimization with Optuna¶
In [50]:
import optuna
optuna.logging.set_verbosity(optuna.logging.WARNING) # use INFO to see progress
In [51]:
def objective(trial):
params_cat_optuna = {
'objective': trial.suggest_categorical('objective', ['Logloss', 'CrossEntropy']),
'colsample_bylevel': trial.suggest_uniform('colsample_bylevel', 0.01, 0.1),
'depth': trial.suggest_int('depth', 1, 12),
'boosting_type': trial.suggest_categorical('boosting_type', ['Ordered', 'Plain']),
'bootstrap_type': trial.suggest_categorical('bootstrap_type',
['Bayesian', 'Bernoulli', 'MVS']),
'used_ram_limit': '3gb'
}
# update parameters
if params_cat_optuna['bootstrap_type'] == 'Bayesian':
params_cat_optuna['bagging_temperature'] = trial.suggest_uniform('bagging_temperature', 0, 10)
elif params_cat_optuna['bootstrap_type'] == 'Bernoulli':
params_cat_optuna['subsample'] = trial.suggest_uniform('subsample', 0.1, 1)
# fit the model
model = CatBoostClassifier(random_state=SEED,**params_cat_optuna)
model.fit(df_Xtrain, ser_ytrain,
eval_set=[(df_Xvalid, ser_yvalid)],
verbose=0,
early_stopping_rounds=100)
ypreds = model.predict(df_Xvalid)
ypreds = np.rint(ypreds)
score = roc_auc_score(ser_yvalid.to_numpy().ravel(),
ypreds)
return score
In [52]:
# NOTE: there is inherent non-determinism in optuna hyperparameter selection
# we may not get the same hyperparameters when run twice.
sampler = optuna.samplers.TPESampler(seed=SEED)
N_TRIALS = 1 # make it large
study = optuna.create_study(direction='maximize',
sampler=sampler,
study_name='cat_optuna',
storage='sqlite:///cat_optuna_fraud_detection.db',
load_if_exists=True)
study.optimize(objective, n_trials=N_TRIALS,timeout=600)
In [53]:
# Resume from last time
sampler = optuna.samplers.TPESampler(seed=SEED)
N_TRIALS = 1 # make it large
study = optuna.create_study(direction='maximize',
sampler=sampler,
study_name='cat_optuna',
storage='sqlite:///cat_optuna_fraud_detection.db',
load_if_exists=True)
# study.optimize(objective, n_trials=N_TRIALS)
In [54]:
print(f'Number of finished trials: {len(study.trials)}')
# best trail
best_trial = study.best_trial
# best params
params_best = study.best_trial.params
params_best
Out[54]:
In [106]:
%%time
model_name = 'catboost'
desc = 'grid search optuna'
Xtr = df_Xtrain_orig
ytr = ser_ytrain_orig.to_numpy().ravel()
Xtx = df_Xtest
ytx = ser_ytest.to_numpy().ravel()
Xvd = df_Xvalid
yvd = ser_yvalid.to_numpy().ravel()
# use best model
params_best = study.best_trial.params
clf = CatBoostClassifier(random_state=SEED,verbose=False)
clf.set_params(**params_best)
# fit and save the model
clf.fit(Xtr, ytr)
joblib.dump(clf,'../models/clf_cat_grid_search_optuna.pkl')
# load the saved model
clf = joblib.load('../models/clf_cat_grid_search_optuna.pkl')
# predictions
ypreds = clf.predict(Xtx)
# model evaluation
cm = confusion_matrix(ytx, ypreds)
print(cm)
desc = f'grid search optuna'
df_eval = model_evaluation('catboost', desc, ytx,ypreds,df_eval=df_eval)
Best Model¶
In [111]:
%%time
model = CatBoostClassifier(verbose=False,random_state=100,
depth=6,
iterations=1_000,
)
model.fit(Xtr, ytr)
joblib.dump(model, '../models/model_cat_best.joblib')
ypreds = model.predict(Xtx)
cm = confusion_matrix(ytest, ypreds)
print(cm)
df_eval = model_evaluation('catboost', 'seed=100,depth=6,iter=1k', ytest, ypreds,df_eval=df_eval)
Model Interpretation¶
In [62]:
df_Xtrain.head(2).append(df_Xtest.head(2))
Out[62]:
Model interpretation using eli5¶
In [63]:
import eli5
eli5.show_weights(model)
Out[63]:
In [64]:
from eli5.sklearn import PermutationImportance
feature_names = df_Xtrain.columns.tolist()
perm = PermutationImportance(model).fit(df_Xtest, ytx)
eli5.show_weights(perm, feature_names=feature_names)
Out[64]:
Model interpretation using lime¶
In [65]:
import lime
import lime.lime_tabular
In [66]:
idx = 0
example = df_Xtest.iloc[idx]
answer = ser_ytest.iloc[idx]
feature_names = df_Xtest.columns.tolist()
prediction = model.predict(example.to_numpy().reshape(-1,1).T)
print(f'answer = {answer}')
print('prediction = ', prediction[0])
print()
print(example)
print(feature_names)
In [67]:
import lime
import lime.lime_tabular
categorical_features = []
categorical_features_idx = [df_Xtrain.columns.get_loc(col) for col in categorical_features]
explainer = lime.lime_tabular.LimeTabularExplainer(df_Xtrain.to_numpy(),
feature_names=feature_names,
class_names=['Not-fraud','Fraud'],
categorical_features=categorical_features_idx,
mode='classification')
exp = explainer.explain_instance(example, model.predict_proba, num_features=8)
exp.show_in_notebook(show_table=True)
In [68]:
exp.as_pyplot_figure(); # use semicolon
Model Evaluation Using shap¶
In [70]:
import shap
shap.initjs()
In [71]:
# model = CatBoostClassifier(verbose=100,random_state=100)
# model.fit(df_Xtrain, ytrain)
model = joblib.load('../models/model_cat_best.joblib')
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(df_Xtest)
In [72]:
df_Xtest.head(1)
Out[72]:
In [73]:
df_Xtest.head(1)['V15 V18 V3 V24 V1 V8 V4 V14 V2 V6 V9 V20'.split()].round(4)
Out[73]:
In [74]:
# Look only first row of test data
# use matplotlib=True to avoid Javascript
idx = 0
shap.force_plot(explainer.expected_value,
shap_values[idx,:],
df_Xtest.iloc[idx,:],
matplotlib=False,
text_rotation=90)
# for this row, the predicted label is -9.33
# red features makes it higher
# blue features makes it smaller.
Out[74]:
In [75]:
NUM = 100
shap.force_plot(explainer.expected_value, shap_values[:NUM,:],
df_Xtest.iloc[:NUM,:],matplotlib=False)
Out[75]:
In [76]:
shap.summary_plot(shap_values, df_Xtest)
In [77]:
shap.summary_plot(shap_values, df_Xtest, plot_type='bar')
In [78]:
shap.dependence_plot("Amount", shap_values, df_Xtest)
In [79]:
shap.dependence_plot(ind='Time', interaction_index='Amount',
shap_values=shap_values,
features=df_Xtest,
display_features=df_Xtest)
Time Taken¶
In [80]:
notebook_end_time = time.time()
time_taken = time.time() - notebook_start_time
h,m = divmod(time_taken,60*60)
print('Time taken to run whole noteook: {:.0f} hr {:.0f} min {:.0f} secs'.format(h, *divmod(m,60)))